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In the present paper we adopt an L2-norm analysis because it can best exhibit the nonanticipating property [1] of the solutions of stochastic differential equations. endobj uuid:676b040c-afb4-11b2-0a00-b01c47020000 Modelling with Stochastic Differential Equations 227 6 .1 Ito Versus Stratonovich 227 6 .2 Diffusion Limits of Markov Chains 229 6 .3 Stochastic Stability 232 6 .4 Parametric Estimation 241 6 .5 Optimal Stochastic Control 244 6 .6 Filtering 248 Chapter 7. 28 0 obj /Type /XObject uuid:676b040d-afb4-11b2-0a00-c05541e2fe7f Abstract This paper gives a review of recent progress in the design of numerical methods for computing the trajectories (sample paths) of solutions to stochastic differential equations. endobj Introduction to Stochastic Time Discrete Approximation.- 10. /ImageMask false There are two types of convergence for a numerical solution of a stochastic differential equation, the strong … $KBR$@��-�C��"��C��g���%we;AQ�b�,qɹ�9g�W�O�߾~��s��^�~����z���/������fcM�l;}�Y_(��ӛMdc�� �=޸��'X��z�y}^e��Y]u|z��:�}���+�����Cg���ؐ����w�u:‹���Ż��c�6?���6�a����vo6� Pl, E. &Heath, D.: 5 0 obj endobj R�EQK8ZcP�Sĺ`�5@��L���"���!�A���#�l�*�Y�@��@��g-@��a1���@�0L�)gTB,�(`�Y��;��(�"酰ˡ�-��v#,�D!�_"� �8 T�t1��T2�J�"���$t8��e�E'bFԳ�! << Probability and Statistics.- 2. 43 0 obj Prince 12.5 (www.princexml.com) /Filter /CCITTFaxDecode @�0��Zkk ��p�f�� ��lP��`�i�����Â#�! Modelling with Stochastic Differential Equations.- 7. �@� �.� /Width 2479 Strong Taylor Approximations.- 11. In this dissertation, we consider the problem of simulation of stochastic differential equations driven by Brownian motions or the general Lévy processes. stream 2020-10-29T16:02:44-07:00 In this text, we consider numerical methods for solving ordinary differential equations, that is, those differential equations that have only one independent variable. /ColorSpace /DeviceGray endobj <> /Height 3504 endobj 59 0 obj Time Discrete Approximation of Deterministic Differential Equations.- 9. Part III. <> �Ea1�����A��]ġ8 Dt" �c#|���Y�A*� /Length 7 0 R 2 0 obj �T���ygFA"�!�a*�4�y���E�N* [48 0 R 50 0 R 52 0 R 54 0 R 55 0 R 56 0 R 57 0 R] 1 D;�I�&�hk�����bc.�H$��V��ou���C#���K��]wQܛ) E�AGd��l��4D�OH��L�?�iU� C��u�A��|�C��. >> <>12]/P 24 0 R/Pg 49 0 R/S/Link>> 4.z_��B""#�R��^>�W�Z�A`�Q���$)��������Xk���tGH������߿�����A@D>��@���+�D$)���������{�gB �8+H����u]�������_��B0����������y'(r1�P�:P��8Dt��on�/"��/��_�#�������I���o�}_����YX*Z�~@������P_넫��$}!tt�YP�v�[ �l���o���~�!HP��O����_�������|">� v����k}���"����K���i �������'~��Ooֿ��U�uD_�Tc���_R��ҵ���-v���u��g <> 地2@��хC$6g:dT��";C%q��)2��'�2�ʂ��"�T2�Re w��EA�.9C�t]��*0Ba$1a���D9��� t]�x���@���,��U��"� xP�(r����~�ˈ�)�L���"9��H�3I #�|��B �0��!/B���qT�q(v�r�T#�Ɯh ��m ��)�A3�DbLL��莋�:��� ��a(p�!�����������P��3�9�XA9|��.�B&�S�GI��*eF @���6P�A��0�[a,p�!��0�A�!<8��V �E:����s�W|���@����'i$(p�B�B�I2���&�� <><>2 3]/P 18 0 R/Pg 49 0 R/S/Link>> Numerical Solution of Stochastic Differential Equations with Jumps in Finance Eckhard Platen School of Finance and Economics and School of Mathematical Sciences University of Technology, Sydney Kloeden, P.E. 2020-10-29T16:02:44-07:00 application/pdf 39 0 obj 8�)�����+Pdp�N����mU+�Q'zuT�l50P��qT"q$�q�#����j8z��I=n'҄I��O� ��f�Ӊ���]Ro���z���4�AB�$0*Oՠ�!�)���%�o�J���u�*{�}uo���rU� ��iS������U%_�j�K�'(Y�Б��@��Kt�"'߼~�Ū��W� /Columns 2479 �u,�B �`�҈A�c�pD��Ô:��a�9J3E�tB�e:��Aa��2�JK9"7��ܳ�!U���2��'D�R��hVGQ.�.� Modelling with Stochastic Differential Equations.- 7. <>stream endobj %PDF-1.7 %���� 1 0 obj endobj Applications of Stochastic Differential Equations.- 8. 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